孟加拉语和孟加拉语:一种单语言数据集,用于在语言多样化的环境中检测情绪
Moshiur Rahman Faisal1, Ashrin Mobashira Shifa1, Md Hasibur Rahman1
1Department of Electrical and Computer Engineering, North South University, Dhaka-1229, Bangladesh.
Data in brief
|August 26, 2024
概括
研究人员开发了一套新的数据集,用于在孟加拉语和孟加拉语中检测情绪,实现了高精度. 这个资源帮助自然语言处理 (NLP) 用于不同的语言背景.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 计算语言学 计算语言学
- 情感计算是一种情感计算.
背景情况:
- 信息技术的进步凸显了NLP中情感检测的必要性.
- 在语言多样化的环境中解释情绪存在挑战,特别是像孟加拉语及其变体孟加拉语这样的低资源语言.
研究的目的:
- 介绍"孟加拉语和孟加拉语"数据集,这是情绪分类的全面资源.
- 为弥补孟加拉语微粒度情绪检测的缺口,并为孟加拉语开创.
- 为了促进孟加拉语到孟加拉语机器翻译研究.
主要方法:
- 创建了一个广泛的数据集,包含六个情感类别的80,098个标记样本.
- 严格的注释和评估流程,以确保数据质量和可靠性.
- 使用BanglaBERT进行绩效评估,实现了重要的指标.
主要成果:
- "孟加拉语和孟加拉语"数据集为孟加拉语提供了细粒度的情感分类,并为孟加拉语引入了情感检测.
- 使用BanglaBERT获得了孟加拉语的71.30%和孟加拉语的64.59%的加权F1得分.
- 证明了高的注释可靠性,科恩的卡帕得分为93.5%.
结论:
- "孟加拉语和孟加拉语"数据集是推动NLP的宝贵资源,特别是在低资源语言的情感检测中.
- 强调在NLP研究和开发中语言多样性的重要性.
- 该数据集支持开发改进的语言处理模型和机器翻译.
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